Overview

Dataset statistics

Number of variables28
Number of observations2524
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory552.2 KiB
Average record size in memory224.1 B

Variable types

Numeric14
Categorical5
Unsupported9

Alerts

nombre has a high cardinality: 2524 distinct values High cardinality
cedula is highly correlated with saldo_total and 1 other fieldsHigh correlation
no_hijos is highly correlated with activos and 2 other fieldsHigh correlation
activos is highly correlated with no_hijos and 3 other fieldsHigh correlation
pasivos is highly correlated with no_hijos and 2 other fieldsHigh correlation
patrimonio is highly correlated with activosHigh correlation
gastos is highly correlated with no_hijos and 2 other fieldsHigh correlation
saldo_total is highly correlated with cedula and 1 other fieldsHigh correlation
mora is highly correlated with cedula and 1 other fieldsHigh correlation
activos is highly correlated with patrimonioHigh correlation
patrimonio is highly correlated with activosHigh correlation
dias_mora is highly correlated with moraHigh correlation
mora is highly correlated with dias_moraHigh correlation
cedula is highly correlated with moraHigh correlation
activos is highly correlated with pasivos and 2 other fieldsHigh correlation
pasivos is highly correlated with activos and 1 other fieldsHigh correlation
patrimonio is highly correlated with activosHigh correlation
gastos is highly correlated with activos and 1 other fieldsHigh correlation
mora is highly correlated with cedulaHigh correlation
antiguedad is highly correlated with edadHigh correlation
edad is highly correlated with antiguedadHigh correlation
activos is highly correlated with patrimonioHigh correlation
patrimonio is highly correlated with activosHigh correlation
salario is highly correlated with saldo_totalHigh correlation
calificacion is highly correlated with dias_moraHigh correlation
dias_mora is highly correlated with calificacion and 1 other fieldsHigh correlation
saldo_total is highly correlated with salarioHigh correlation
mora is highly correlated with dias_moraHigh correlation
activos is highly skewed (γ1 = 38.47219861) Skewed
patrimonio is highly skewed (γ1 = 38.47257644) Skewed
gastos is highly skewed (γ1 = 31.83263709) Skewed
mora is highly skewed (γ1 = 23.1708264) Skewed
cedula is uniformly distributed Uniform
nombre is uniformly distributed Uniform
cedula has unique values Unique
nombre has unique values Unique
tipo_vivienda is an unsupported type, check if it needs cleaning or further analysis Unsupported
ocupacion is an unsupported type, check if it needs cleaning or further analysis Unsupported
nivel_academico is an unsupported type, check if it needs cleaning or further analysis Unsupported
personas_a_cargo is an unsupported type, check if it needs cleaning or further analysis Unsupported
tiene_casa is an unsupported type, check if it needs cleaning or further analysis Unsupported
tiene_carro is an unsupported type, check if it needs cleaning or further analysis Unsupported
estado_civil is an unsupported type, check if it needs cleaning or further analysis Unsupported
tipo_contrato is an unsupported type, check if it needs cleaning or further analysis Unsupported
entidad is an unsupported type, check if it needs cleaning or further analysis Unsupported
antiguedad has 138 (5.5%) zeros Zeros
no_hijos has 1214 (48.1%) zeros Zeros
activos has 1005 (39.8%) zeros Zeros
pasivos has 1037 (41.1%) zeros Zeros
patrimonio has 1153 (45.7%) zeros Zeros
gastos has 882 (34.9%) zeros Zeros
dias_mora has 2266 (89.8%) zeros Zeros
saldo_total has 206 (8.2%) zeros Zeros
mora has 157 (6.2%) zeros Zeros

Reproduction

Analysis started2022-02-19 22:06:09.725688
Analysis finished2022-02-19 22:06:45.854548
Duration36.13 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

cedula
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct2524
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1271500
Minimum10000
Maximum2533000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.8 KiB
2022-02-19T17:06:45.981132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile136150
Q1640750
median1271500
Q31902250
95-th percentile2406850
Maximum2533000
Range2523000
Interquartile range (IQR)1261500

Descriptive statistics

Standard deviation728760.363
Coefficient of variation (CV)0.5731501085
Kurtosis-1.2
Mean1271500
Median Absolute Deviation (MAD)631000
Skewness1.34333 × 10-16
Sum3209266000
Variance5.310916667 × 1011
MonotonicityStrictly increasing
2022-02-19T17:06:46.123128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100001
 
< 0.1%
16960001
 
< 0.1%
16890001
 
< 0.1%
16900001
 
< 0.1%
16910001
 
< 0.1%
16920001
 
< 0.1%
16930001
 
< 0.1%
16940001
 
< 0.1%
16950001
 
< 0.1%
16970001
 
< 0.1%
Other values (2514)2514
99.6%
ValueCountFrequency (%)
100001
< 0.1%
110001
< 0.1%
120001
< 0.1%
130001
< 0.1%
140001
< 0.1%
150001
< 0.1%
160001
< 0.1%
170001
< 0.1%
180001
< 0.1%
190001
< 0.1%
ValueCountFrequency (%)
25330001
< 0.1%
25320001
< 0.1%
25310001
< 0.1%
25300001
< 0.1%
25290001
< 0.1%
25280001
< 0.1%
25270001
< 0.1%
25260001
< 0.1%
25250001
< 0.1%
25240001
< 0.1%

nombre
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct2524
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size19.8 KiB
JORGE ELIECER ALDANA AMADOR
 
1
YASMINE ESTERLING RODRIGUEZ
 
1
ROSALBA BARRERA GUTIERREZ
 
1
FRANCIA ELENA SALAZAR SALAZAR
 
1
MARTHA YASMIN HERNANDEZ MORENO
 
1
Other values (2519)
2519 

Length

Max length39
Median length26
Mean length25.85697306
Min length10

Characters and Unicode

Total characters65263
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2524 ?
Unique (%)100.0%

Sample

1st rowJORGE ELIECER ALDANA AMADOR
2nd rowELKIN ARLEY CASTRO VELANDIA
3rd rowHERSSON FERNANDO FLOREZ MORENO
4th rowBRAYAN ALEXANDER BARON ORTEGON
5th rowCINDY MAYELA OCHOA HERNANDEZ

Common Values

ValueCountFrequency (%)
JORGE ELIECER ALDANA AMADOR1
 
< 0.1%
YASMINE ESTERLING RODRIGUEZ1
 
< 0.1%
ROSALBA BARRERA GUTIERREZ1
 
< 0.1%
FRANCIA ELENA SALAZAR SALAZAR1
 
< 0.1%
MARTHA YASMIN HERNANDEZ MORENO1
 
< 0.1%
LUZ AMPARO CORDOBA PALACIOS1
 
< 0.1%
RAYNER EDUARDO LOZADA LOZANO1
 
< 0.1%
RAQUEL AMADO MARTINEZ1
 
< 0.1%
HECTOR MIGUEL TORRES GUETTE1
 
< 0.1%
MARIA EUGENIA REATIGA BARAJAS1
 
< 0.1%
Other values (2514)2514
99.6%

Length

2022-02-19T17:06:46.249273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maria146
 
1.6%
rodriguez125
 
1.3%
luz94
 
1.0%
hernandez82
 
0.9%
martinez80
 
0.9%
jose79
 
0.8%
moreno73
 
0.8%
gonzalez66
 
0.7%
garcia66
 
0.7%
luis65
 
0.7%
Other values (2129)8508
90.7%

Most occurring characters

ValueCountFrequency (%)
A9315
14.3%
6865
10.5%
R5924
 
9.1%
E5835
 
8.9%
O4671
 
7.2%
I4368
 
6.7%
N3909
 
6.0%
L3623
 
5.6%
S2468
 
3.8%
D2260
 
3.5%
Other values (21)16025
24.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter58196
89.2%
Space Separator6865
 
10.5%
Initial Punctuation200
 
0.3%
Other Punctuation1
 
< 0.1%
Control1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A9315
16.0%
R5924
10.2%
E5835
10.0%
O4671
 
8.0%
I4368
 
7.5%
N3909
 
6.7%
L3623
 
6.2%
S2468
 
4.2%
D2260
 
3.9%
M1920
 
3.3%
Other values (17)13903
23.9%
Space Separator
ValueCountFrequency (%)
6865
100.0%
Initial Punctuation
ValueCountFrequency (%)
200
100.0%
Other Punctuation
ValueCountFrequency (%)
1
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin58196
89.2%
Common7067
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A9315
16.0%
R5924
10.2%
E5835
10.0%
O4671
 
8.0%
I4368
 
7.5%
N3909
 
6.7%
L3623
 
6.2%
S2468
 
4.2%
D2260
 
3.9%
M1920
 
3.3%
Other values (17)13903
23.9%
Common
ValueCountFrequency (%)
6865
97.1%
200
 
2.8%
1
 
< 0.1%
1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII64859
99.4%
None203
 
0.3%
Punctuation201
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A9315
14.4%
6865
10.6%
R5924
 
9.1%
E5835
 
9.0%
O4671
 
7.2%
I4368
 
6.7%
N3909
 
6.0%
L3623
 
5.6%
S2468
 
3.8%
D2260
 
3.5%
Other values (17)15621
24.1%
None
ValueCountFrequency (%)
Ã202
99.5%
1
 
0.5%
Punctuation
ValueCountFrequency (%)
200
99.5%
1
 
0.5%

antiguedad
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct36
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.457210777
Minimum0
Maximum36
Zeros138
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size19.8 KiB
2022-02-19T17:06:46.370927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q310
95-th percentile24
Maximum36
Range36
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.348317631
Coefficient of variation (CV)0.9853976039
Kurtosis2.208946704
Mean7.457210777
Median Absolute Deviation (MAD)3
Skewness1.581106633
Sum18822
Variance53.997772
MonotonicityNot monotonic
2022-02-19T17:06:46.511518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2325
12.9%
3245
 
9.7%
1240
 
9.5%
5231
 
9.2%
4211
 
8.4%
6182
 
7.2%
0138
 
5.5%
7124
 
4.9%
887
 
3.4%
1168
 
2.7%
Other values (26)673
26.7%
ValueCountFrequency (%)
0138
5.5%
1240
9.5%
2325
12.9%
3245
9.7%
4211
8.4%
5231
9.2%
6182
7.2%
7124
 
4.9%
887
 
3.4%
952
 
2.1%
ValueCountFrequency (%)
3616
0.6%
355
 
0.2%
345
 
0.2%
324
 
0.2%
313
 
0.1%
309
0.4%
295
 
0.2%
2812
0.5%
2718
0.7%
2619
0.8%

activo
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.8 KiB
Activo
2523 
Pendiente de validación
 
1

Length

Max length24
Median length6
Mean length6.007131537
Min length6

Characters and Unicode

Total characters15162
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowActivo
2nd rowActivo
3rd rowActivo
4th rowActivo
5th rowActivo

Common Values

ValueCountFrequency (%)
Activo2523
> 99.9%
Pendiente de validación1
 
< 0.1%

Length

2022-02-19T17:06:46.620869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-19T17:06:46.745871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
activo2523
99.9%
pendiente1
 
< 0.1%
de1
 
< 0.1%
validaciã³n1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i2526
16.7%
c2524
16.6%
t2524
16.6%
v2524
16.6%
A2523
16.6%
o2523
16.6%
e4
 
< 0.1%
n3
 
< 0.1%
d3
 
< 0.1%
2
 
< 0.1%
Other values (5)6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12634
83.3%
Uppercase Letter2525
 
16.7%
Space Separator2
 
< 0.1%
Other Number1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i2526
20.0%
c2524
20.0%
t2524
20.0%
v2524
20.0%
o2523
20.0%
e4
 
< 0.1%
n3
 
< 0.1%
d3
 
< 0.1%
a2
 
< 0.1%
l1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
A2523
99.9%
P1
 
< 0.1%
Ã1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Number
ValueCountFrequency (%)
³1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15159
> 99.9%
Common3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i2526
16.7%
c2524
16.7%
t2524
16.7%
v2524
16.7%
A2523
16.6%
o2523
16.6%
e4
 
< 0.1%
n3
 
< 0.1%
d3
 
< 0.1%
a2
 
< 0.1%
Other values (3)3
 
< 0.1%
Common
ValueCountFrequency (%)
2
66.7%
³1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII15160
> 99.9%
None2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i2526
16.7%
c2524
16.6%
t2524
16.6%
v2524
16.6%
A2523
16.6%
o2523
16.6%
e4
 
< 0.1%
n3
 
< 0.1%
d3
 
< 0.1%
2
 
< 0.1%
Other values (3)4
 
< 0.1%
None
ValueCountFrequency (%)
Ã1
50.0%
³1
50.0%

ciudad
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size19.8 KiB
GRAN
1058 
VILLA
846 
ACA
507 
SM
112 
GRA
 
1

Length

Max length5
Median length4
Mean length4.045166403
Min length2

Characters and Unicode

Total characters10210
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowGRA
2nd rowGRAN
3rd rowGRAN
4th rowACA
5th rowGRAN

Common Values

ValueCountFrequency (%)
GRAN1058
41.9%
VILLA846
33.5%
ACA507
20.1%
SM112
 
4.4%
GRA1
 
< 0.1%

Length

2022-02-19T17:06:46.881757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-19T17:06:46.975751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
gran1058
41.9%
villa846
33.5%
aca507
20.1%
sm112
 
4.4%
gra1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A2919
28.6%
L1692
16.6%
G1059
 
10.4%
R1059
 
10.4%
N1058
 
10.4%
V846
 
8.3%
I846
 
8.3%
C507
 
5.0%
S112
 
1.1%
M112
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10210
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A2919
28.6%
L1692
16.6%
G1059
 
10.4%
R1059
 
10.4%
N1058
 
10.4%
V846
 
8.3%
I846
 
8.3%
C507
 
5.0%
S112
 
1.1%
M112
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin10210
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A2919
28.6%
L1692
16.6%
G1059
 
10.4%
R1059
 
10.4%
N1058
 
10.4%
V846
 
8.3%
I846
 
8.3%
C507
 
5.0%
S112
 
1.1%
M112
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A2919
28.6%
L1692
16.6%
G1059
 
10.4%
R1059
 
10.4%
N1058
 
10.4%
V846
 
8.3%
I846
 
8.3%
C507
 
5.0%
S112
 
1.1%
M112
 
1.1%

edad
Real number (ℝ≥0)

HIGH CORRELATION

Distinct60
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.43779715
Minimum19
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.8 KiB
2022-02-19T17:06:47.065555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile28
Q137
median46
Q355
95-th percentile65
Maximum80
Range61
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.71739553
Coefficient of variation (CV)0.2523245341
Kurtosis-0.7365723054
Mean46.43779715
Median Absolute Deviation (MAD)9
Skewness0.07877517473
Sum117209
Variance137.297358
MonotonicityNot monotonic
2022-02-19T17:06:47.190525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3982
 
3.2%
3781
 
3.2%
4380
 
3.2%
4976
 
3.0%
4473
 
2.9%
4872
 
2.9%
4171
 
2.8%
5071
 
2.8%
4570
 
2.8%
4070
 
2.8%
Other values (50)1778
70.4%
ValueCountFrequency (%)
191
 
< 0.1%
203
 
0.1%
216
 
0.2%
227
 
0.3%
2316
0.6%
2417
0.7%
2520
0.8%
2614
 
0.6%
2726
1.0%
2835
1.4%
ValueCountFrequency (%)
801
 
< 0.1%
792
 
0.1%
763
 
0.1%
754
 
0.2%
743
 
0.1%
735
 
0.2%
725
 
0.2%
718
0.3%
708
0.3%
6915
0.6%

genero
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.8 KiB
Femenino
1622 
Masculino
902 

Length

Max length9
Median length8
Mean length8.357369255
Min length8

Characters and Unicode

Total characters21094
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMasculino
2nd rowMasculino
3rd rowMasculino
4th rowMasculino
5th rowFemenino

Common Values

ValueCountFrequency (%)
Femenino1622
64.3%
Masculino902
35.7%

Length

2022-02-19T17:06:47.299881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-19T17:06:47.362360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
femenino1622
64.3%
masculino902
35.7%

Most occurring characters

ValueCountFrequency (%)
n4146
19.7%
e3244
15.4%
i2524
12.0%
o2524
12.0%
F1622
 
7.7%
m1622
 
7.7%
M902
 
4.3%
a902
 
4.3%
s902
 
4.3%
c902
 
4.3%
Other values (2)1804
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter18570
88.0%
Uppercase Letter2524
 
12.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n4146
22.3%
e3244
17.5%
i2524
13.6%
o2524
13.6%
m1622
 
8.7%
a902
 
4.9%
s902
 
4.9%
c902
 
4.9%
u902
 
4.9%
l902
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
F1622
64.3%
M902
35.7%

Most occurring scripts

ValueCountFrequency (%)
Latin21094
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n4146
19.7%
e3244
15.4%
i2524
12.0%
o2524
12.0%
F1622
 
7.7%
m1622
 
7.7%
M902
 
4.3%
a902
 
4.3%
s902
 
4.3%
c902
 
4.3%
Other values (2)1804
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII21094
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n4146
19.7%
e3244
15.4%
i2524
12.0%
o2524
12.0%
F1622
 
7.7%
m1622
 
7.7%
M902
 
4.3%
a902
 
4.3%
s902
 
4.3%
c902
 
4.3%
Other values (2)1804
8.6%

tipo_vivienda
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size19.8 KiB

estrato
Real number (ℝ≥0)

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.188193344
Minimum0
Maximum6
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size19.8 KiB
2022-02-19T17:06:47.424846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8207592363
Coefficient of variation (CV)0.3750853363
Kurtosis0.3996171532
Mean2.188193344
Median Absolute Deviation (MAD)1
Skewness0.397895582
Sum5523
Variance0.673645724
MonotonicityNot monotonic
2022-02-19T17:06:47.502952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
21180
46.8%
3736
29.2%
1495
19.6%
493
 
3.7%
514
 
0.6%
03
 
0.1%
63
 
0.1%
ValueCountFrequency (%)
03
 
0.1%
1495
19.6%
21180
46.8%
3736
29.2%
493
 
3.7%
514
 
0.6%
63
 
0.1%
ValueCountFrequency (%)
63
 
0.1%
514
 
0.6%
493
 
3.7%
3736
29.2%
21180
46.8%
1495
19.6%
03
 
0.1%

ocupacion
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size19.8 KiB

nivel_academico
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size19.8 KiB

no_hijos
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.080031696
Minimum0
Maximum11
Zeros1214
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size19.8 KiB
2022-02-19T17:06:47.924732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.266951425
Coefficient of variation (CV)1.173068744
Kurtosis1.512745582
Mean1.080031696
Median Absolute Deviation (MAD)1
Skewness1.090012756
Sum2726
Variance1.605165913
MonotonicityNot monotonic
2022-02-19T17:06:48.002838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
01214
48.1%
2556
22.0%
1398
 
15.8%
3247
 
9.8%
482
 
3.2%
520
 
0.8%
66
 
0.2%
111
 
< 0.1%
ValueCountFrequency (%)
01214
48.1%
1398
 
15.8%
2556
22.0%
3247
 
9.8%
482
 
3.2%
520
 
0.8%
66
 
0.2%
111
 
< 0.1%
ValueCountFrequency (%)
111
 
< 0.1%
66
 
0.2%
520
 
0.8%
482
 
3.2%
3247
 
9.8%
2556
22.0%
1398
 
15.8%
01214
48.1%

personas_a_cargo
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size19.8 KiB

tiene_casa
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size19.8 KiB

tiene_carro
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size19.8 KiB

estado_civil
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size19.8 KiB

tipo_contrato
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size19.8 KiB

activos
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct262
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219982523.1
Minimum0
Maximum2.50007 × 1011
Zeros1005
Zeros (%)39.8%
Negative0
Negative (%)0.0%
Memory size19.8 KiB
2022-02-19T17:06:48.112189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5000000
Q388250000
95-th percentile250000000
Maximum2.50007 × 1011
Range2.50007 × 1011
Interquartile range (IQR)88250000

Descriptive statistics

Standard deviation5802217285
Coefficient of variation (CV)26.37581024
Kurtosis1538.062468
Mean219982523.1
Median Absolute Deviation (MAD)5000000
Skewness38.47219861
Sum5.552358882 × 1011
Variance3.366572542 × 1019
MonotonicityNot monotonic
2022-02-19T17:06:48.259197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01005
39.8%
1126
 
5.0%
15000000092
 
3.6%
5000000066
 
2.6%
10000000065
 
2.6%
20000000063
 
2.5%
500000052
 
2.1%
8000000041
 
1.6%
3000000041
 
1.6%
6000000040
 
1.6%
Other values (252)933
37.0%
ValueCountFrequency (%)
01005
39.8%
1126
 
5.0%
23
 
0.1%
42
 
0.1%
351
 
< 0.1%
1000003
 
0.1%
1500001
 
< 0.1%
2000002
 
0.1%
2400001
 
< 0.1%
3000003
 
0.1%
ValueCountFrequency (%)
2.50007 × 10111
< 0.1%
1.5 × 10111
< 0.1%
40500000001
< 0.1%
20100000001
< 0.1%
12000000001
< 0.1%
9550000001
< 0.1%
8500000002
0.1%
8443000001
< 0.1%
8000000001
< 0.1%
7400000001
< 0.1%

pasivos
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct288
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27934840.26
Minimum0
Maximum2500000000
Zeros1037
Zeros (%)41.1%
Negative0
Negative (%)0.0%
Memory size19.8 KiB
2022-02-19T17:06:48.384167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median965000
Q340000000
95-th percentile120000000
Maximum2500000000
Range2500000000
Interquartile range (IQR)40000000

Descriptive statistics

Standard deviation69282001.47
Coefficient of variation (CV)2.480128786
Kurtosis648.0347743
Mean27934840.26
Median Absolute Deviation (MAD)965000
Skewness19.27261303
Sum7.050753682 × 1010
Variance4.799995728 × 1015
MonotonicityNot monotonic
2022-02-19T17:06:48.509138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01037
41.1%
1126
 
5.0%
5000000062
 
2.5%
3000000056
 
2.2%
10000000050
 
2.0%
4000000048
 
1.9%
2000000047
 
1.9%
6000000046
 
1.8%
8000000044
 
1.7%
1000000035
 
1.4%
Other values (278)973
38.5%
ValueCountFrequency (%)
01037
41.1%
1126
 
5.0%
25
 
0.2%
245001
 
< 0.1%
300001
 
< 0.1%
500001
 
< 0.1%
1000009
 
0.4%
1500002
 
0.1%
1800001
 
< 0.1%
2000006
 
0.2%
ValueCountFrequency (%)
25000000001
 
< 0.1%
7000000001
 
< 0.1%
5000000001
 
< 0.1%
4500000002
 
0.1%
3600000001
 
< 0.1%
3312000001
 
< 0.1%
3000000001
 
< 0.1%
2650000001
 
< 0.1%
2500000005
0.2%
2400000001
 
< 0.1%

patrimonio
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct603
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190867161.4
Minimum-2325000000
Maximum2.49952 × 1011
Zeros1153
Zeros (%)45.7%
Negative355
Negative (%)14.1%
Memory size19.8 KiB
2022-02-19T17:06:48.705812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2325000000
5-th percentile-38000000
Q10
median0
Q333700000
95-th percentile191700000
Maximum2.49952 × 1011
Range2.52277 × 1011
Interquartile range (IQR)33700000

Descriptive statistics

Standard deviation5801898577
Coefficient of variation (CV)30.39757355
Kurtosis1537.970322
Mean190867161.4
Median Absolute Deviation (MAD)3000000
Skewness38.47257644
Sum4.817487154 × 1011
Variance3.36620271 × 1019
MonotonicityNot monotonic
2022-02-19T17:06:48.940131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01153
45.7%
2000000036
 
1.4%
7000000033
 
1.3%
1000000030
 
1.2%
5000000027
 
1.1%
3000000027
 
1.1%
10000000019
 
0.8%
15000000015
 
0.6%
100000015
 
0.6%
500000015
 
0.6%
Other values (593)1154
45.7%
ValueCountFrequency (%)
-23250000001
< 0.1%
-6550000001
< 0.1%
-2200000001
< 0.1%
-1500000001
< 0.1%
-1486000001
< 0.1%
-1400000001
< 0.1%
-1300000001
< 0.1%
-1270000001
< 0.1%
-1200000001
< 0.1%
-1155000001
< 0.1%
ValueCountFrequency (%)
2.49952 × 10111
< 0.1%
1.499995 × 10111
< 0.1%
40470000001
< 0.1%
15100000001
< 0.1%
11750000001
< 0.1%
8000000001
< 0.1%
7300000001
< 0.1%
7250000001
< 0.1%
6410000001
< 0.1%
6000000001
< 0.1%

salario
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1329
Distinct (%)52.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3181291.113
Minimum1
Maximum50919989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.8 KiB
2022-02-19T17:06:49.166881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1167812
Q11896063
median2633097
Q33919989
95-th percentile6940665.25
Maximum50919989
Range50919988
Interquartile range (IQR)2023926

Descriptive statistics

Standard deviation2270543.211
Coefficient of variation (CV)0.7137175223
Kurtosis101.6720346
Mean3181291.113
Median Absolute Deviation (MAD)900835.5
Skewness6.629771286
Sum8029578769
Variance5.155366472 × 1012
MonotonicityNot monotonic
2022-02-19T17:06:49.291852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
220967987
 
3.4%
189606348
 
1.9%
229002641
 
1.6%
439864340
 
1.6%
162154331
 
1.2%
364192729
 
1.1%
175570429
 
1.1%
204082828
 
1.1%
424431426
 
1.0%
400000025
 
1.0%
Other values (1319)2140
84.8%
ValueCountFrequency (%)
12
0.1%
6160001
 
< 0.1%
6894541
 
< 0.1%
6894551
 
< 0.1%
7082301
 
< 0.1%
7318521
 
< 0.1%
7377171
 
< 0.1%
7400001
 
< 0.1%
7812424
0.2%
7917691
 
< 0.1%
ValueCountFrequency (%)
509199891
< 0.1%
355550001
< 0.1%
250000001
< 0.1%
215677081
< 0.1%
192000001
< 0.1%
190000001
< 0.1%
167452111
< 0.1%
152242821
< 0.1%
129199891
< 0.1%
128000001
< 0.1%

gastos
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct280
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1392213.583
Minimum0
Maximum301500000
Zeros882
Zeros (%)34.9%
Negative0
Negative (%)0.0%
Memory size19.8 KiB
2022-02-19T17:06:49.416822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median755000
Q31585000
95-th percentile3643450
Maximum301500000
Range301500000
Interquartile range (IQR)1585000

Descriptive statistics

Standard deviation7233586.331
Coefficient of variation (CV)5.195744691
Kurtosis1222.356657
Mean1392213.583
Median Absolute Deviation (MAD)755000
Skewness31.83263709
Sum3513947083
Variance5.232477121 × 1013
MonotonicityNot monotonic
2022-02-19T17:06:49.541794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0882
34.9%
1000000188
 
7.4%
2000000123
 
4.9%
1500000104
 
4.1%
800000101
 
4.0%
50000093
 
3.7%
60000087
 
3.4%
120000074
 
2.9%
70000066
 
2.6%
300000057
 
2.3%
Other values (270)749
29.7%
ValueCountFrequency (%)
0882
34.9%
15
 
0.2%
41
 
< 0.1%
10000011
 
0.4%
1400001
 
< 0.1%
2000009
 
0.4%
2200001
 
< 0.1%
2500002
 
0.1%
30000033
 
1.3%
3275001
 
< 0.1%
ValueCountFrequency (%)
3015000001
< 0.1%
1190000001
< 0.1%
1025000001
< 0.1%
629260001
< 0.1%
610000001
< 0.1%
515000001
< 0.1%
400000021
< 0.1%
300000001
< 0.1%
279900001
< 0.1%
166000001
< 0.1%

entidad
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size19.8 KiB

no_productos
Real number (ℝ≥0)

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.54318542
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.8 KiB
2022-02-19T17:06:49.698004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7752341748
Coefficient of variation (CV)0.5023597066
Kurtosis5.769445993
Mean1.54318542
Median Absolute Deviation (MAD)0
Skewness1.804606453
Sum3895
Variance0.6009880259
MonotonicityNot monotonic
2022-02-19T17:06:49.854187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
11492
59.1%
2768
30.4%
3208
 
8.2%
443
 
1.7%
510
 
0.4%
62
 
0.1%
91
 
< 0.1%
ValueCountFrequency (%)
11492
59.1%
2768
30.4%
3208
 
8.2%
443
 
1.7%
510
 
0.4%
62
 
0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
62
 
0.1%
510
 
0.4%
443
 
1.7%
3208
 
8.2%
2768
30.4%
11492
59.1%

calificacion
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size19.8 KiB
A
2391 
E
 
78
B
 
32
D
 
15
C
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2524
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A2391
94.7%
E78
 
3.1%
B32
 
1.3%
D15
 
0.6%
C8
 
0.3%

Length

2022-02-19T17:06:50.010397image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-19T17:06:50.088537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
a2391
94.7%
e78
 
3.1%
b32
 
1.3%
d15
 
0.6%
c8
 
0.3%

Most occurring characters

ValueCountFrequency (%)
A2391
94.7%
E78
 
3.1%
B32
 
1.3%
D15
 
0.6%
C8
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2524
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A2391
94.7%
E78
 
3.1%
B32
 
1.3%
D15
 
0.6%
C8
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin2524
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A2391
94.7%
E78
 
3.1%
B32
 
1.3%
D15
 
0.6%
C8
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2524
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A2391
94.7%
E78
 
3.1%
B32
 
1.3%
D15
 
0.6%
C8
 
0.3%

dias_mora
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct63
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.99207607
Minimum0
Maximum3630
Zeros2266
Zeros (%)89.8%
Negative0
Negative (%)0.0%
Memory size19.8 KiB
2022-02-19T17:06:50.166647image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile55
Maximum3630
Range3630
Interquartile range (IQR)0

Descriptive statistics

Standard deviation181.3668933
Coefficient of variation (CV)6.255740117
Kurtosis118.6926099
Mean28.99207607
Median Absolute Deviation (MAD)0
Skewness9.634143471
Sum73176
Variance32893.95
MonotonicityNot monotonic
2022-02-19T17:06:50.293272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02266
89.8%
25107
 
4.2%
5526
 
1.0%
3020
 
0.8%
856
 
0.2%
1755
 
0.2%
2354
 
0.2%
4504
 
0.2%
2954
 
0.2%
5703
 
0.1%
Other values (53)79
 
3.1%
ValueCountFrequency (%)
02266
89.8%
25107
 
4.2%
3020
 
0.8%
5526
 
1.0%
603
 
0.1%
856
 
0.2%
903
 
0.1%
1153
 
0.1%
1453
 
0.1%
1501
 
< 0.1%
ValueCountFrequency (%)
36301
< 0.1%
24591
< 0.1%
21521
< 0.1%
19801
< 0.1%
18301
< 0.1%
17951
< 0.1%
17401
< 0.1%
17351
< 0.1%
16801
< 0.1%
16751
< 0.1%

saldo_total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2271
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10443854.11
Minimum0
Maximum257318078
Zeros206
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size19.8 KiB
2022-02-19T17:06:50.433862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1605599.5
median2106686.5
Q310442965.75
95-th percentile49406588.3
Maximum257318078
Range257318078
Interquartile range (IQR)9837366.25

Descriptive statistics

Standard deviation19720873.12
Coefficient of variation (CV)1.88827543
Kurtosis20.95712341
Mean10443854.11
Median Absolute Deviation (MAD)1937246
Skewness3.691864227
Sum2.636028778 × 1010
Variance3.889128366 × 1014
MonotonicityNot monotonic
2022-02-19T17:06:50.558833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0206
 
8.2%
75045027
 
1.1%
5003005
 
0.2%
4002404
 
0.2%
5503303
 
0.1%
3502102
 
0.1%
68097562
 
0.1%
6003602
 
0.1%
4047522
 
0.1%
3001802
 
0.1%
Other values (2261)2269
89.9%
ValueCountFrequency (%)
0206
8.2%
21
 
< 0.1%
5611
 
< 0.1%
11491
 
< 0.1%
14851
 
< 0.1%
59511
 
< 0.1%
233651
 
< 0.1%
261461
 
< 0.1%
469611
 
< 0.1%
470941
 
< 0.1%
ValueCountFrequency (%)
2573180781
< 0.1%
1789851441
< 0.1%
1589883931
< 0.1%
1543316581
< 0.1%
1346660761
< 0.1%
1336452641
< 0.1%
1297497911
< 0.1%
1292450371
< 0.1%
1194743941
< 0.1%
1113603981
< 0.1%

mora
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct2292
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1197997.478
Minimum0
Maximum163603870
Zeros157
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size19.8 KiB
2022-02-19T17:06:50.699427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1235551.25
median532156
Q31253856.5
95-th percentile3284678.15
Maximum163603870
Range163603870
Interquartile range (IQR)1018305.25

Descriptive statistics

Standard deviation4587891.652
Coefficient of variation (CV)3.829633814
Kurtosis699.2558484
Mean1197997.478
Median Absolute Deviation (MAD)383000.5
Skewness23.1708264
Sum3023745635
Variance2.104874981 × 1013
MonotonicityIncreasing
2022-02-19T17:06:50.840016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0157
 
6.2%
38558427
 
1.1%
2056455
 
0.2%
2570565
 
0.2%
5286583
 
0.1%
2827623
 
0.1%
1799393
 
0.1%
6646643
 
0.1%
3536043
 
0.1%
1071452
 
0.1%
Other values (2282)2313
91.6%
ValueCountFrequency (%)
0157
6.2%
1741
 
< 0.1%
21602
 
0.1%
30002
 
0.1%
62711
 
< 0.1%
90002
 
0.1%
111571
 
< 0.1%
120001
 
< 0.1%
128441
 
< 0.1%
175921
 
< 0.1%
ValueCountFrequency (%)
1636038701
< 0.1%
784335071
< 0.1%
751324541
< 0.1%
558300061
< 0.1%
522203451
< 0.1%
452657051
< 0.1%
312835611
< 0.1%
254267251
< 0.1%
253589481
< 0.1%
244590901
< 0.1%

Interactions

2022-02-19T17:06:42.996123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:15.493712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:17.420391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:21.847192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:23.477098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:25.236144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:27.027390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:29.085531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:30.784751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:32.594455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:34.606619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:36.285505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:38.819226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:40.985745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:43.117897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:15.781513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:17.560570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:21.957129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:23.586115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:25.368281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:27.153682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:29.202687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:30.923955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:32.709204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:34.721716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:36.408481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:39.003337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:41.147989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:43.257645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:15.928893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:17.719928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:22.079621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:23.719861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:25.502923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:27.288314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:29.324110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:31.070768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:32.850165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:34.847693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:36.555211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:39.207166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:41.268877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:43.407771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-19T17:06:22.195160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:23.888691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-19T17:06:31.318014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:33.113483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-19T17:06:36.909513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:39.445432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:41.788161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:43.669804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:16.348127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-19T17:06:16.466565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-19T17:06:22.539743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-19T17:06:28.010128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:29.793289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:31.567707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:33.371770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:35.332870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:37.329429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-19T17:06:42.034705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:43.929239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:16.580955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:18.416289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-19T17:06:26.161802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:28.132804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:29.919675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:31.696271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:33.483523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:35.433664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:37.497938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:39.948737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:42.161390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:44.048884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:16.691911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:18.561208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-19T17:06:24.475883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:26.288465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:28.255202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:30.032156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:31.801992image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:33.609911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:35.564070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-19T17:06:44.419581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:17.060740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-19T17:06:30.406034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-19T17:06:40.523494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:42.625136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:44.552244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:17.181973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:21.539080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:23.245190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:24.980868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:26.768542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:28.835726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:30.533009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:32.341342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:34.368297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:36.041350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:38.482410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:40.698432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:42.751730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:44.670526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:17.295091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:21.672065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:23.354333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:25.106867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:26.896771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:28.952090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:30.647867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:32.462830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:34.488035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:36.159250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:38.629383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:40.820366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-19T17:06:42.867683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-02-19T17:06:50.964985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-19T17:06:51.168065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-19T17:06:51.355520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-19T17:06:51.544960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-19T17:06:51.685552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-19T17:06:44.974877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-19T17:06:45.690063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

cedulanombreantiguedadactivociudadedadgenerotipo_viviendaestratoocupacionnivel_academicono_hijospersonas_a_cargotiene_casatiene_carroestado_civiltipo_contratoactivospasivospatrimoniosalariogastosentidadno_productoscalificaciondias_morasaldo_totalmora
010000JORGE ELIECER ALDANA AMADOR4ActivoGRA54MasculinoARRENDADA2DOCENTEUniversitario4Cuatro o mas00CasadoA término Indefinido11011499061000000GOB MT1A0492687.00
111000ELKIN ARLEY CASTRO VELANDIA1ActivoGRAN29MasculinoPROPIA2EMPLEADO PUBLICO NO DOCENTETécnico0Cero00Union LibreA término Indefinido10000000016000000012289251000000EJER NAL1A0395715.00
212000HERSSON FERNANDO FLOREZ MORENO6ActivoGRAN29MasculinoFAMILIAR3DOCENTETécnico1UnaNNSolteroA término Fijo184000000-8399999923194091540000GOB MT1A0603910.00
313000BRAYAN ALEXANDER BARON ORTEGON0ActivoACA31MasculinoARRENDADA2TecnicoUniversitario0Cero00SolteroA término Indefinido0001854448900000MPIO EL DORA1A0631766.00
414000CINDY MAYELA OCHOA HERNANDEZ5ActivoGRAN34FemeninoPROPIA2DOCENTEEspecializacion1Una00CasadoA término Indefinido150000000670000008300000024891021000000GOB MT1A00.00
515000LEIDY JOHANA PARRADO GALINDEZ17ActivoGRAN36FemeninoPROPIA1DOCENTE SECUNDARIAUniversitario1UnaNNUnion LibreA término Indefinido2000000001000000001000000007576449800000GOB MT1A038563398.00
616000CAMILO ANDRES HIGUAVITA CORREA0ActivoVILLA36MasculinoARRENDADA3CONTRATISTATecnológico1UnaNNSolteroA término Indefinido35000000100000002189163800000MUNICIPIO DE PTO GAI1A0333834.00
717000LEIDY ALEXANDRA NARANJO LOZANO0ActivoGRAN39FemeninoPROPIA2CONTADOR (A)Universitario2TresNNSeparaci?n JudicialA término Fijo1020000002000000002511300600000SOC SALEC1A0556867.00
818000MARIA LUZ DARY ABRIL NIÑO7ActivoACA55FemeninoARRENDADA2DOCENTEEspecializacion3Una00SolteroA término Indefinido1000000060000000-500000004244314800000GOB MT1A0100640.00
919000ALONSO BONILLA NIÑO0ActivoACA61MasculinoPROPIA4TECNOLOGOTecnológico0UnaNNCasadoA término Fijo2400000003500000020500000025769251000000HOSP ACA1A00.00

Last rows

cedulanombreantiguedadactivociudadedadgenerotipo_viviendaestratoocupacionnivel_academicono_hijospersonas_a_cargotiene_casatiene_carroestado_civiltipo_contratoactivospasivospatrimoniosalariogastosentidadno_productoscalificaciondias_morasaldo_totalmora
25142524000EMMA IDE ALFONSO ALFONSO13ActivoVILLA42FemeninoPROPIA1OtrosUniversitario0Cero00Union LibreA término Indefinido00024065190GOB MT3E95513574761.024459090
25152525000ROSA ELENA CARDOZO HERMOSA9ActivoGRAN54FemeninoPROPIA2OtrosBachillerato0Una00SolteroA término Indefinido0007917690MPIO JUAN9A303649086.025358948
25162526000MARLY YISETH BUITRAGO MONTEALEGRE9ActivoGRAN31FemeninoARRENDADA1OtrosTecnológico0Cero00CasadoA término Fijo0008500000CONC S JUAN3E125512703438.025426725
25172527000FLOR DEL CARMEN MEDINA MARTIN6ActivoVILLA55FemeninoPROPIA3OtrosPostgrado0Una00CasadoOtra00061331820PREVI S.A.1E96020906958.031283561
25182528000GUILLERMO TOBON BORRERO6ActivoVILLA62MasculinoPROPIA3OtrosPostgrado0Cero00CasadoA término Indefinido00031203360MUN VCIO4E173513884066.045265705
25192529000OFELIA PEREZ21ActivoGRAN63FemeninoARRENDADA2OtrosTecnológico0Cero00Union Libre00001167812002E99012678497.052220345
25202530000DORIS EDIT PARRADO PARRADO7ActivoGRAN56FemeninoPROPIA3OtrosTécnico0Cero00Soltero00003300000001E174026341773.055830006
25212531000LUIS HERLEY JIMENEZ VALENCIA6ActivoVILLA53MasculinoPROPIA3OtrosBachillerato0Cero00CasadoA término Indefinido00024029000MUN VCIO6E134526442992.075132454
25222532000ANGELICA MARIA BLANCO QUINTERO13ActivoVILLA43FemeninoPROPIA3OtrosPostgrado0Cero00CasadoA término Indefinido00058725900MUN VCIO3E86517119909.078433507
25232533000LUIS MIGUEL DIAZ CUBIDES21ActivoVILLA61MasculinoPROPIA3OtrosPostgrado0Cero00Union Libre00002129772001E363039753981.0163603870